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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.24394 |
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| _version_ | 1866917048016175104 |
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| author | Barragán, Sandra Pérez-Bote, Adrián Sáez, Carlos Salgado, David Sanguiao-Sande, Luis |
| author_facet | Barragán, Sandra Pérez-Bote, Adrián Sáez, Carlos Salgado, David Sanguiao-Sande, Luis |
| contents | We provide a description of pilot and production experiences to streamline some business functions in the official statistical production process using statistical learning models. Our approach is quality-oriented searching for an improvement on accuracy, cost-efficiency, timeliness, granularity, response burden reduction, and frequency. Pilot experiences have been conducted with data from real surveys in Statistics Spain (INE). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24394 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Streamlining business functions in official statistical production with Machine Learning Barragán, Sandra Pérez-Bote, Adrián Sáez, Carlos Salgado, David Sanguiao-Sande, Luis Applications Methodology We provide a description of pilot and production experiences to streamline some business functions in the official statistical production process using statistical learning models. Our approach is quality-oriented searching for an improvement on accuracy, cost-efficiency, timeliness, granularity, response burden reduction, and frequency. Pilot experiences have been conducted with data from real surveys in Statistics Spain (INE). |
| title | Streamlining business functions in official statistical production with Machine Learning |
| topic | Applications Methodology |
| url | https://arxiv.org/abs/2510.24394 |